Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory344.7 KiB
Average record size in memory353.0 B

Variable types

DateTime1
Text2
Categorical2
Numeric6

Alerts

Source Latitude is highly overall correlated with Source LongitudeHigh correlation
Source Longitude is highly overall correlated with Source LatitudeHigh correlation
Timestamp has unique values Unique
Source IP has unique values Unique
Destination IP has unique values Unique

Reproduction

Analysis started2025-02-20 14:50:07.772775
Analysis finished2025-02-20 14:50:09.509812
Duration1.74 second
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Timestamp
Date

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2025-01-06 00:00:00
Maximum2025-02-16 15:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-20T09:50:09.546567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.591688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Source IP
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
2025-02-20T09:50:09.724713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.202
Min length10

Characters and Unicode

Total characters13202
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row3.103.66.246
2nd row113.0.159.154
3rd row29.177.12.9
4th row35.15.147.90
5th row37.143.134.196
ValueCountFrequency (%)
71.131.192.250 1
 
0.1%
139.46.139.59 1
 
0.1%
3.103.66.246 1
 
0.1%
113.0.159.154 1
 
0.1%
29.177.12.9 1
 
0.1%
35.15.147.90 1
 
0.1%
37.143.134.196 1
 
0.1%
100.197.188.55 1
 
0.1%
134.204.76.80 1
 
0.1%
125.108.113.48 1
 
0.1%
Other values (990) 990
99.0%
2025-02-20T09:50:09.893888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.7%
1 2505
19.0%
2 1644
12.5%
4 823
 
6.2%
3 803
 
6.1%
5 794
 
6.0%
0 764
 
5.8%
9 748
 
5.7%
6 727
 
5.5%
8 713
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2505
19.0%
2 1644
12.5%
4 823
 
6.2%
3 803
 
6.1%
5 794
 
6.0%
0 764
 
5.8%
9 748
 
5.7%
6 727
 
5.5%
8 713
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2505
19.0%
2 1644
12.5%
4 823
 
6.2%
3 803
 
6.1%
5 794
 
6.0%
0 764
 
5.8%
9 748
 
5.7%
6 727
 
5.5%
8 713
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2505
19.0%
2 1644
12.5%
4 823
 
6.2%
3 803
 
6.1%
5 794
 
6.0%
0 764
 
5.8%
9 748
 
5.7%
6 727
 
5.5%
8 713
 
5.4%

Destination IP
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
2025-02-20T09:50:10.002976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.202
Min length9

Characters and Unicode

Total characters13202
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row179.237.113.27
2nd row118.239.10.133
3rd row163.155.10.217
4th row55.25.44.11
5th row3.187.29.211
ValueCountFrequency (%)
221.111.134.190 1
 
0.1%
111.112.131.249 1
 
0.1%
179.237.113.27 1
 
0.1%
118.239.10.133 1
 
0.1%
163.155.10.217 1
 
0.1%
55.25.44.11 1
 
0.1%
3.187.29.211 1
 
0.1%
195.39.152.205 1
 
0.1%
88.43.230.166 1
 
0.1%
96.238.186.112 1
 
0.1%
Other values (990) 990
99.0%
2025-02-20T09:50:10.143425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.7%
1 2518
19.1%
2 1611
12.2%
4 841
 
6.4%
3 830
 
6.3%
5 819
 
6.2%
7 739
 
5.6%
9 734
 
5.6%
8 712
 
5.4%
6 704
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2518
19.1%
2 1611
12.2%
4 841
 
6.4%
3 830
 
6.3%
5 819
 
6.2%
7 739
 
5.6%
9 734
 
5.6%
8 712
 
5.4%
6 704
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2518
19.1%
2 1611
12.2%
4 841
 
6.4%
3 830
 
6.3%
5 819
 
6.2%
7 739
 
5.6%
9 734
 
5.6%
8 712
 
5.4%
6 704
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3000
22.7%
1 2518
19.1%
2 1611
12.2%
4 841
 
6.4%
3 830
 
6.3%
5 819
 
6.2%
7 739
 
5.6%
9 734
 
5.6%
8 712
 
5.4%
6 704
 
5.3%

Attack Type
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size57.0 KiB
Phishing
176 
DDoS
176 
Malware
168 
Ransomware
166 
Insider Threat
158 

Length

Max length14
Median length10
Mean length9.188
Min length4

Characters and Unicode

Total characters9188
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInsider Threat
2nd rowInsider Threat
3rd rowMalware
4th rowDDoS
5th rowDDoS

Common Values

ValueCountFrequency (%)
Phishing 176
17.6%
DDoS 176
17.6%
Malware 168
16.8%
Ransomware 166
16.6%
Insider Threat 158
15.8%
SQL Injection 156
15.6%

Length

2025-02-20T09:50:10.174692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T09:50:10.220896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
phishing 176
13.4%
ddos 176
13.4%
malware 168
12.8%
ransomware 166
12.6%
insider 158
12.0%
threat 158
12.0%
sql 156
11.9%
injection 156
11.9%

Most occurring characters

ValueCountFrequency (%)
a 826
 
9.0%
n 812
 
8.8%
e 806
 
8.8%
i 666
 
7.2%
r 650
 
7.1%
h 510
 
5.6%
s 500
 
5.4%
o 498
 
5.4%
D 352
 
3.8%
w 334
 
3.6%
Other values (16) 3234
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 826
 
9.0%
n 812
 
8.8%
e 806
 
8.8%
i 666
 
7.2%
r 650
 
7.1%
h 510
 
5.6%
s 500
 
5.4%
o 498
 
5.4%
D 352
 
3.8%
w 334
 
3.6%
Other values (16) 3234
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 826
 
9.0%
n 812
 
8.8%
e 806
 
8.8%
i 666
 
7.2%
r 650
 
7.1%
h 510
 
5.6%
s 500
 
5.4%
o 498
 
5.4%
D 352
 
3.8%
w 334
 
3.6%
Other values (16) 3234
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 826
 
9.0%
n 812
 
8.8%
e 806
 
8.8%
i 666
 
7.2%
r 650
 
7.1%
h 510
 
5.6%
s 500
 
5.4%
o 498
 
5.4%
D 352
 
3.8%
w 334
 
3.6%
Other values (16) 3234
35.2%

Severity
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.1 KiB
Critical
261 
Low
255 
High
245 
Medium
239 

Length

Max length8
Median length6
Mean length5.267
Min length3

Characters and Unicode

Total characters5267
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowCritical
4th rowLow
5th rowCritical

Common Values

ValueCountFrequency (%)
Critical 261
26.1%
Low 255
25.5%
High 245
24.5%
Medium 239
23.9%

Length

2025-02-20T09:50:10.259521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-20T09:50:10.275147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
critical 261
26.1%
low 255
25.5%
high 245
24.5%
medium 239
23.9%

Most occurring characters

ValueCountFrequency (%)
i 1006
19.1%
C 261
 
5.0%
r 261
 
5.0%
t 261
 
5.0%
c 261
 
5.0%
a 261
 
5.0%
l 261
 
5.0%
L 255
 
4.8%
o 255
 
4.8%
w 255
 
4.8%
Other values (8) 1930
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5267
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1006
19.1%
C 261
 
5.0%
r 261
 
5.0%
t 261
 
5.0%
c 261
 
5.0%
a 261
 
5.0%
l 261
 
5.0%
L 255
 
4.8%
o 255
 
4.8%
w 255
 
4.8%
Other values (8) 1930
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5267
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1006
19.1%
C 261
 
5.0%
r 261
 
5.0%
t 261
 
5.0%
c 261
 
5.0%
a 261
 
5.0%
l 261
 
5.0%
L 255
 
4.8%
o 255
 
4.8%
w 255
 
4.8%
Other values (8) 1930
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5267
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1006
19.1%
C 261
 
5.0%
r 261
 
5.0%
t 261
 
5.0%
c 261
 
5.0%
a 261
 
5.0%
l 261
 
5.0%
L 255
 
4.8%
o 255
 
4.8%
w 255
 
4.8%
Other values (8) 1930
36.6%

Attempt Count
Real number (ℝ)

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.025
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-20T09:50:10.320825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5107843
Coefficient of variation (CV)0.54970417
Kurtosis-1.1811198
Mean10.025
Median Absolute Deviation (MAD)5
Skewness0.0032340051
Sum10025
Variance30.368744
MonotonicityNot monotonic
2025-02-20T09:50:10.344621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
19 63
 
6.3%
16 62
 
6.2%
10 60
 
6.0%
8 58
 
5.8%
6 57
 
5.7%
9 56
 
5.6%
12 56
 
5.6%
2 56
 
5.6%
1 55
 
5.5%
11 55
 
5.5%
Other values (9) 422
42.2%
ValueCountFrequency (%)
1 55
5.5%
2 56
5.6%
3 53
5.3%
4 47
4.7%
5 43
4.3%
6 57
5.7%
7 46
4.6%
8 58
5.8%
9 56
5.6%
10 60
6.0%
ValueCountFrequency (%)
19 63
6.3%
18 45
4.5%
17 54
5.4%
16 62
6.2%
15 40
4.0%
14 48
4.8%
13 46
4.6%
12 56
5.6%
11 55
5.5%
10 60
6.0%

Data Volume (MB)
Real number (ℝ)

Distinct628
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.045
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-02-20T09:50:10.393886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile50
Q1268.75
median504.5
Q3728
95-th percentile954.1
Maximum999
Range997
Interquartile range (IQR)459.25

Descriptive statistics

Standard deviation282.69606
Coefficient of variation (CV)0.56647408
Kurtosis-1.0870347
Mean499.045
Median Absolute Deviation (MAD)230.5
Skewness0.011353094
Sum499045
Variance79917.062
MonotonicityNot monotonic
2025-02-20T09:50:10.461105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 6
 
0.6%
556 6
 
0.6%
328 6
 
0.6%
805 5
 
0.5%
537 5
 
0.5%
34 5
 
0.5%
28 4
 
0.4%
57 4
 
0.4%
594 4
 
0.4%
803 4
 
0.4%
Other values (618) 951
95.1%
ValueCountFrequency (%)
2 1
 
0.1%
5 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
15 2
0.2%
16 3
0.3%
17 1
 
0.1%
18 1
 
0.1%
ValueCountFrequency (%)
999 1
 
0.1%
998 1
 
0.1%
997 4
0.4%
996 2
0.2%
994 2
0.2%
992 2
0.2%
990 2
0.2%
989 2
0.2%
988 2
0.2%
987 1
 
0.1%

Source Latitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.639337
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative220
Negative (%)22.0%
Memory size7.9 KiB
2025-02-20T09:50:10.503013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)15.2305

Descriptive statistics

Standard deviation32.286241
Coefficient of variation (CV)1.2119761
Kurtosis-0.26000825
Mean26.639337
Median Absolute Deviation (MAD)6.6606
Skewness-1.2186716
Sum26639.337
Variance1042.4013
MonotonicityNot monotonic
2025-02-20T09:50:10.539407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-27.4698 85
 
8.5%
51.5074 82
 
8.2%
45.5017 74
 
7.4%
-37.8136 74
 
7.4%
35.0116 68
 
6.8%
55.9533 68
 
6.8%
34.6937 67
 
6.7%
40.7128 66
 
6.6%
41.8781 66
 
6.6%
35.6895 64
 
6.4%
Other values (5) 286
28.6%
ValueCountFrequency (%)
-37.8136 74
7.4%
-33.8688 61
6.1%
-27.4698 85
8.5%
34.0522 63
6.3%
34.6937 67
6.7%
35.0116 68
6.8%
35.6895 64
6.4%
40.7128 66
6.6%
41.8781 66
6.6%
43.651 59
5.9%
ValueCountFrequency (%)
55.9533 68
6.8%
53.4808 49
4.9%
51.5074 82
8.2%
49.2827 54
5.4%
45.5017 74
7.4%
43.651 59
5.9%
41.8781 66
6.6%
40.7128 66
6.6%
35.6895 64
6.4%
35.0116 68
6.8%

Source Longitude
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.980866
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative581
Negative (%)58.1%
Memory size7.9 KiB
2025-02-20T09:50:10.572343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-74.006
median-0.1278
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)213.6977

Descriptive statistics

Standard deviation106.77383
Coefficient of variation (CV)4.2742245
Kurtosis-1.7034766
Mean24.980866
Median Absolute Deviation (MAD)118.1159
Skewness0.03960214
Sum24980.866
Variance11400.65
MonotonicityNot monotonic
2025-02-20T09:50:10.594584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
153.0251 85
 
8.5%
-0.1278 82
 
8.2%
-73.5673 74
 
7.4%
144.9631 74
 
7.4%
135.7681 68
 
6.8%
-3.1883 68
 
6.8%
135.5023 67
 
6.7%
-74.006 66
 
6.6%
-87.6298 66
 
6.6%
139.6917 64
 
6.4%
Other values (5) 286
28.6%
ValueCountFrequency (%)
-123.1207 54
5.4%
-118.2437 63
6.3%
-87.6298 66
6.6%
-79.347 59
5.9%
-74.006 66
6.6%
-73.5673 74
7.4%
-3.1883 68
6.8%
-2.2426 49
4.9%
-0.1278 82
8.2%
135.5023 67
6.7%
ValueCountFrequency (%)
153.0251 85
8.5%
151.2093 61
6.1%
144.9631 74
7.4%
139.6917 64
6.4%
135.7681 68
6.8%
135.5023 67
6.7%
-0.1278 82
8.2%
-2.2426 49
4.9%
-3.1883 68
6.8%
-73.5673 74
7.4%

Destination Latitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.331025
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative202
Negative (%)20.2%
Memory size7.9 KiB
2025-02-20T09:50:10.711134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)15.2305

Descriptive statistics

Standard deviation31.628275
Coefficient of variation (CV)1.116383
Kurtosis0.061075899
Mean28.331025
Median Absolute Deviation (MAD)6.6606
Skewness-1.3321182
Sum28331.025
Variance1000.3478
MonotonicityNot monotonic
2025-02-20T09:50:10.747971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
53.4808 79
 
7.9%
43.651 75
 
7.5%
55.9533 72
 
7.2%
-27.4698 72
 
7.2%
34.0522 72
 
7.2%
-37.8136 69
 
6.9%
45.5017 68
 
6.8%
51.5074 68
 
6.8%
35.0116 65
 
6.5%
40.7128 64
 
6.4%
Other values (5) 296
29.6%
ValueCountFrequency (%)
-37.8136 69
6.9%
-33.8688 61
6.1%
-27.4698 72
7.2%
34.0522 72
7.2%
34.6937 61
6.1%
35.0116 65
6.5%
35.6895 53
5.3%
40.7128 64
6.4%
41.8781 60
6.0%
43.651 75
7.5%
ValueCountFrequency (%)
55.9533 72
7.2%
53.4808 79
7.9%
51.5074 68
6.8%
49.2827 61
6.1%
45.5017 68
6.8%
43.651 75
7.5%
41.8781 60
6.0%
40.7128 64
6.4%
35.6895 53
5.3%
35.0116 65
6.5%

Destination Longitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.351159
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative619
Negative (%)61.9%
Memory size7.9 KiB
2025-02-20T09:50:10.776758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-79.347
median-2.2426
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)219.0387

Descriptive statistics

Standard deviation105.72216
Coefficient of variation (CV)6.093089
Kurtosis-1.6395706
Mean17.351159
Median Absolute Deviation (MAD)116.0011
Skewness0.15940795
Sum17351.159
Variance11177.175
MonotonicityNot monotonic
2025-02-20T09:50:10.804340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-2.2426 79
 
7.9%
-79.347 75
 
7.5%
-3.1883 72
 
7.2%
153.0251 72
 
7.2%
-118.2437 72
 
7.2%
144.9631 69
 
6.9%
-73.5673 68
 
6.8%
-0.1278 68
 
6.8%
135.7681 65
 
6.5%
-74.006 64
 
6.4%
Other values (5) 296
29.6%
ValueCountFrequency (%)
-123.1207 61
6.1%
-118.2437 72
7.2%
-87.6298 60
6.0%
-79.347 75
7.5%
-74.006 64
6.4%
-73.5673 68
6.8%
-3.1883 72
7.2%
-2.2426 79
7.9%
-0.1278 68
6.8%
135.5023 61
6.1%
ValueCountFrequency (%)
153.0251 72
7.2%
151.2093 61
6.1%
144.9631 69
6.9%
139.6917 53
5.3%
135.7681 65
6.5%
135.5023 61
6.1%
-0.1278 68
6.8%
-2.2426 79
7.9%
-3.1883 72
7.2%
-73.5673 68
6.8%

Interactions

2025-02-20T09:50:09.150071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:07.903491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.140519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.391424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.697122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.930391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.188069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:07.943111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.182746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.431866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.739084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.958138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.226664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:07.983902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.208765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.473384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.782131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.003989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.258759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.023749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.258537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.571356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.809154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.038944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.299322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.059164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.293994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.612368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.856461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.074711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.336458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.091470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.337899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.654366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:08.892592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-20T09:50:09.108593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-20T09:50:10.832667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Attack TypeAttempt CountData Volume (MB)Destination LatitudeDestination LongitudeSeveritySource LatitudeSource Longitude
Attack Type1.0000.0330.0000.0300.0410.0000.0000.000
Attempt Count0.0331.000-0.0350.020-0.0060.0000.018-0.013
Data Volume (MB)0.000-0.0351.0000.022-0.0500.000-0.1070.083
Destination Latitude0.0300.0200.0221.000-0.4890.0310.051-0.046
Destination Longitude0.041-0.006-0.050-0.4891.0000.000-0.0130.006
Severity0.0000.0000.0000.0310.0001.0000.0000.000
Source Latitude0.0000.018-0.1070.051-0.0130.0001.000-0.552
Source Longitude0.000-0.0130.083-0.0460.0060.000-0.5521.000

Missing values

2025-02-20T09:50:09.394630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-20T09:50:09.442043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
02025-01-06 00:00:003.103.66.246179.237.113.27Insider ThreatMedium9208-33.8688151.209343.6510-79.3470
12025-01-06 01:00:00113.0.159.154118.239.10.133Insider ThreatMedium142045.5017-73.5673-33.8688151.2093
22025-01-06 02:00:0029.177.12.9163.155.10.217MalwareCritical16949.2827-123.1207-33.8688151.2093
32025-01-06 03:00:0035.15.147.9055.25.44.11DDoSLow539134.0522-118.243735.0116135.7681
42025-01-06 04:00:0037.143.134.1963.187.29.211DDoSCritical1089955.9533-3.188345.5017-73.5673
52025-01-06 05:00:00100.197.188.55195.39.152.205Insider ThreatCritical260541.8781-87.629834.6937135.5023
62025-01-06 06:00:0061.57.200.3105.115.148.255MalwareHigh478-27.4698153.025143.6510-79.3470
72025-01-06 07:00:00189.155.119.13640.57.46.190SQL InjectionCritical1139345.5017-73.5673-37.8136144.9631
82025-01-06 08:00:0071.131.192.250221.111.134.190PhishingCritical367435.0116135.768153.4808-2.2426
92025-01-06 09:00:0084.192.205.182182.201.59.170PhishingCritical116255.9533-3.188355.9533-3.1883
TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
9902025-02-16 06:00:0073.38.233.18681.253.2.74DDoSCritical295651.5074-0.127845.5017-73.5673
9912025-02-16 07:00:0068.35.52.183162.17.77.133MalwareCritical666743.6510-79.347055.9533-3.1883
9922025-02-16 08:00:00162.200.91.19777.132.156.116MalwareCritical732743.6510-79.347051.5074-0.1278
9932025-02-16 09:00:00163.134.60.22824.152.168.233SQL InjectionCritical1086049.2827-123.120743.6510-79.3470
9942025-02-16 10:00:00112.22.41.233106.143.148.85MalwareLow1541434.6937135.502351.5074-0.1278
9952025-02-16 11:00:00117.89.71.2293.176.189.141RansomwareMedium1396-37.8136144.963141.8781-87.6298
9962025-02-16 12:00:00200.202.151.78182.88.37.88MalwareLow1928-37.8136144.963134.6937135.5023
9972025-02-16 13:00:00119.248.18.68166.241.68.111Insider ThreatHigh1654335.6895139.6917-37.8136144.9631
9982025-02-16 14:00:00136.63.45.12192.4.189.89DDoSCritical6940-33.8688151.2093-33.8688151.2093
9992025-02-16 15:00:00139.46.139.59111.112.131.249MalwareMedium2547-33.8688151.209335.0116135.7681